Dataset Information

Modeling Continuous Admixture Using Admixture-Induced Linkage Disequilibrium.

ABSTRACT: Recent migrations and inter-ethnic mating of long isolated populations have resulted in genetically admixed populations. To understand the complex population admixture process, which is critical to both evolutionary and medical studies, here we used admixture induced linkage disequilibrium (LD) to infer continuous admixture events, which is common for most existing admixed populations. Unlike previous studies, we expanded the typical continuous admixture model to a more general scenario with isolation after a certain duration of continuous gene flow. Based on the new models, we developed a method, CAMer, to infer the admixture history considering continuous and complex demographic process of gene flow between populations. We evaluated the performance of CAMer by computer simulation and further applied our method to real data analysis of a few well-known admixed populations.

SUBMITTER: Zhou Y

PROVIDER: S-EPMC5322361 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Publications

Modeling Continuous Admixture Using Admixture-Induced Linkage Disequilibrium.

Zhou Ying Y Qiu Hongxiang H Xu Shuhua S

Scientific reports 20170223

Recent migrations and inter-ethnic mating of long isolated populations have resulted in genetically admixed populations. To understand the complex population admixture process, which is critical to both evolutionary and medical studies, here we used admixture induced linkage disequilibrium (LD) to infer continuous admixture events, which is common for most existing admixed populations. Unlike previous studies, we expanded the typical continuous admixture model to a more general scenario with iso ...[more]

PMID: 28230170

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Project description:Mapping by admixture linkage disequilibrium (MALD) is a potentially powerful technique for the mapping of complex genetic diseases. The practical requirements of this method include (a) a set of markers spanning the genome that have large allele-frequency differences between the parental ethnicities contributing to the admixed population and (b) an understanding of the extent of admixture in the study population. To this end, a DNA-pooling technique was used to screen microsatellite and diallelic insertion/deletion markers for allele-frequency differences between putative representatives of the parental populations of the admixed Mexican American (MA) and African American (AA) populations. Markers with promising pooled differences were then confirmed by individual genotyping in both the parental and admixed populations. For the MA population, screening of >600 markers identified 151 ethnic-difference markers (EDMs) with delta>0.30 (where delta is the absolute value of each allele-frequency difference between two populations, summed over all marker alleles and divided by two) that are likely to be useful for MALD analysis. For the AA population, analysis of >400 markers identified 97 EDMs. In addition, individual genotyping of these markers in Pima Amerindians, Yavapai Amerindians, European American (EA) individuals, Africans from Zimbabwe, MA individuals, and AA individuals, as well as comparison to the CEPH genotyping set, suggests that the differences between subpopulations of an ethnicity are small for many markers with large interethnic differences. Estimates of admixture that are based on individual genotyping of these markers are consistent with a 60% EA:40% Amerindian contribution to MA populations and with a 20% EA:80% African contribution to AA populations. Taken together, these data suggest that EDMs with large interpopulation and small intrapopulation differences can be readily identified for MALD studies in both AA and MA populations.

Project description:MotivationThe completion of the genome has paved the way for genome-wide association studies (GWAS), which explained certain proportions of heritability. GWAS are not optimally suited to detect non-linear effects in disease risk, possibly hidden in non-additive interactions (epistasis). Alternative methods for epistasis detection using, e.g. deep neural networks (DNNs) are currently under active development. However, DNNs are constrained by finite computational resources, which can be rapidly depleted due to increasing complexity with the sheer size of the genome. Besides, the curse of dimensionality complicates the task of capturing meaningful genetic patterns for DNNs; therefore necessitates dimensionality reduction.ResultsWe propose a method to compress single nucleotide polymorphism (SNP) data, while leveraging the linkage disequilibrium (LD) structure and preserving potential epistasis. This method involves clustering correlated SNPs into haplotype blocks and training per-block autoencoders to learn a compressed representation of the block's genetic content. We provide an adjustable autoencoder design to accommodate diverse blocks and bypass extensive hyperparameter tuning. We applied this method to genotyping data from Project MinE, and achieved 99% average test reconstruction accuracy-i.e. minimal information loss-while compressing the input to nearly 10% of the original size. We demonstrate that haplotype-block based autoencoders outperform linear Principal Component Analysis (PCA) by approximately 3% chromosome-wide accuracy of reconstructed variants. To the extent of our knowledge, our approach is the first to simultaneously leverage haplotype structure and DNNs for dimensionality reduction of genetic data.Availability and implementationData are available for academic use through Project MinE at https://www.projectmine.com/research/data-sharing/, contingent upon terms and requirements specified by the source studies. Code is available at https://github.com/gizem-tas/haploblock-autoencoders.

Dataset Information

Modeling Continuous Admixture Using Admixture-Induced Linkage Disequilibrium.

Publications

Modeling Continuous Admixture Using Admixture-Induced Linkage Disequilibrium.

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